Automated control and optimization of laser-driven ion acceleration

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2FCZ______%3A_____%2F23%3AN0000039" target="_blank" >RIV/CZ______:_____/23:N0000039 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.cambridge.org/core/journals/high-power-laser-science-and-engineering/article/automated-control-and-optimization-of-laserdriven-ion-acceleration/067E7D12CC7461E51E51B426BC7BDC40" target="_blank" >https://www.cambridge.org/core/journals/high-power-laser-science-and-engineering/article/automated-control-and-optimization-of-laserdriven-ion-acceleration/067E7D12CC7461E51E51B426BC7BDC40</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1017/hpl.2023.23" target="_blank" >10.1017/hpl.2023.23</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automated control and optimization of laser-driven ion acceleration

Popis výsledku v původním jazyce

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimization. Here, an automated, HRR-compatible system produced high-fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimization of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.

Název v anglickém jazyce

Automated control and optimization of laser-driven ion acceleration

Popis výsledku anglicky

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimization. Here, an automated, HRR-compatible system produced high-fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimization of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10306 - Optics (including laser optics and quantum optics)

Projekt

<a href="/cs/project/EF16_019%2F0000789" target="_blank" >EF16_019/0000789: Pokročilý výzkum s využitím fotonů a částic vytvořených vysoce intenzivními lasery</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

High Power Laser Science and Engineering

ISSN

2095-4719

e-ISSN

2052-3289

Svazek periodika

11

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

CN - Čínská lidová republika

Počet stran výsledku

9

Strana od-do

e35, PII S2095471923000233

Kód UT WoS článku

000988440000001

EID výsledku v databázi Scopus

2-s2.0-85151549946